Project Summary/Abstract Dysfunction of fronto-striatal circuits involving orbitofrontal cortex (OFC) and caudate nucleus (CN) have been implicated in a wide array of neuropsychiatric disorders, including mood disorders [1-5], schizophrenia [6-9], and obsessive-compulsive disorder [10-15]. As development of novel pharmaceutical treatments for these disorders has stalled [16-18], focus has shifted to novel approaches, such as computational psychiatry [19], which seeks to explain neuropsychiatric diseases in terms of underlying computational processes. Several neuroimaging studies suggest that negative symptoms in schizophrenic patients are correlated with disruptions in fronto-striatal circuits involved in expected value signaling [20-22]. The current grant aims to understand which computations occur in the OFC-caudate cortico-striatal circuit as these regions translate reward- predictive cues into a decision. The proposed studies utilize electrophysiological and neural decoding methods during a behavioral task in which subjects choose between two alternatives. Our hypothesis is that, during binary choices, CN acts to integrate evidence for one or other choice response based on input from OFC ensembles as they vacillate between decodable states representing the two options. Once this evidence reaches some criterion, the choice response is implemented. We will test our hypothesis with the following two specific aims: Aim 1. Interaction between OFC and CN: translating value into action. We plan to record simultaneously from ensembles of OFC and CN neurons and use decoding algorithms to examine how the dynamics of the putative choice response signal in CN relates to OFC vacillation representing the two alternative outcomes. The value of these outcomes must be derived from two outcome properties: reward amount and reward probability. We will utilize GLM-based multi- label classification methods to examine how this value is represented, and linear discriminant analysis (LDA) to decode how a choice is signaled. Aim 2. Causal manipulation of choice response through closed-loop control. Our second aim focuses on testing the causal role of the computations output by OFC state fluctuations and CN integration in decision-making. We plan to decode OFC vacillation in real time and instruct the animal to make its response when the decoder is consistent with OFC representing either the high or low value option, and measure the behavioral effect of this manipulation on choice responses and reaction times. In other words, we will use the output of the OFC decoder to control the timing of the animal?s choice response, effectively closing the loop between neural activity and behavior.